Model‐coupled GRACE‐based analysis of hydrological dynamics of drying Lake Urmia and its basin

Abstract

Lake Urmia basin (LUB), in northwestern Iran, is under the influence of extreme degradation due to a number of natural and anthropogenic factors. The existence of the Lake is critical for the microclimate of the region as well as the quality of human life and wildlife, which necessitates an up-to-date and holistic analysis of its hydrological dynamics. In this premise, satellite-based terrestrial water storage (TWS) received from the Gravity Recovery and Climate Experiment (GRACE) mission was coupled with hydrometeorological modelling and assessment tools to analyse the hydrological status of the lake and its basin. As a new gap-filling approach, the Seasonal-Trend decomposition using Locally estimated scatterplot smoothing (LOESS) (STL) decomposition technique was proposed in this study to reconstruct the missing TWS data.Integrating satellite precipitation data with the Catchment Land Surface Model (CLSM) and WaterGAP model outputs, the hydrological status of the lake was investigated. The STL-based TWS turned out to concord well with the simulated TWS from the CLSM indicating the acceptable performance of the proposed technique.The findings revealed that the LUB had undergone an alarming hydrological situation from 2003 to 2021 with a total loss of 10 and 7:56 km 3 from its TWS and groundwater storage (GWS), respectively. The water level time series also indicated that the water level of the lake had diminished with an annual rate of À70 AE 21 cm=year corresponding to a total water level depletion of about 13:35 AE 3:9 m during the 2003-2021 period. The GRACE-derived TWS and GWS also agreed well with the CLSM simulations. Assessment of the extreme events of the LUB suggested that the basin suffered from a severe dry event in 2008 resulting in the depletion of its water storage and water level. It was also found that from 2003 onward, a critical hydrological setting had dominated the LUB with a negative hydrological balance of À0:96 km 3 .

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

“…These metrics indicated a strong agreement between the predicted and observed GRACE TWSA values, affirming the reliability of the trained STL model. Previous studies have also shown the effectiveness of the STL method in reconstructing TWSA data (Ali, Ran, Luan, et al, 2024;Khorrami, Ali, Sahin, & Gunduz, 2023). The trained STL model was further applied to reconstruct TWSA between GRACE and GRACE-FO mission for all months with missing data (Figure S4b in Supporting Information S1).…”

“…The Seasonal Trend Loess (STL) method (Cleveland et al., 1990) was used to reconstruct the TWS anomalies for the missing months. The STL approach was chosen because of its simplicity, computational efficiency, and documented reliability in previous studies (Ali, Ran, Luan, et al., 2024; Khorrami, Ali, Sahin, & Gunduz, 2023). STL was applied on the monthly time series of each GRACE grid, decomposing it into trends, seasonality, and residuals as follows: $$${\text{TWS}}_{t}={T}_{t}+{S}_{t}+{R}_{t}$$$ where TWS, T , S , and R represent the original TWS, trend, seasonality, and residuals at time t , respectively.…”

Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine

“…Consequently, the inflow of water into the lake has decreased 26 . Thus, the alarming shrinkage of the lake can be attributed to a combination of climatic extremes and anthropogenic activities, along with unsustainable water management 27 . To address the environmental tragedy of the LU, the Lake Urmia Restoration Program (ULRP) was initiated in 2013 as a ten-year program.…”

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

“…The GRACE observations have so far been applied for the largescale assessment of water storage, groundwater storage, and drought (e.g., Abhishek, 2022;Ali et al, 2023;Guo et al, 2021;Khorrami, Ali, Sahin, & Gunduz, 2023;Khorrami & Gunduz, 2021a;Khorrami & Gündüz, 2022;Li et al, 2019;Liu, Pei, & Shen, 2022;Tariq et al, 2023;Wang et al, 2020;Wang et al, 2022;Zhao et al, 2022). The GRACE-based drought indicators such as total storage deficit index (TSDI) (Yirdaw et al, 2008), water storage deficit (WSD) (Thomas et al, 2014), GRACE-based hydrological drought index (GHDI) (Yi & Wen, 2016), GRACE-drought severity index (GDSI) , Water Storage Deficit Index (WSDI) (Sinha et al, 2017), Modified Total Storage Deficit Index (MTSDI) (Hosseini-Moghari et al, 2019), combined climatologic deviation index (CCDI) (Sinha et al, 2019), and enhanced water storage deficit index (EWSDI) (Khorrami & Gunduz, 2021a) have been developed and used worldwide.…”